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Exploring Potential Barrier Estimation Mechanism Based on Quantum Dynamics Framework

Chinese Journal of Electronics 2025年第1期34卷 350-364页

作者： Quan Tang Peng Wang Chengdu Institution of Computer Application Chinese Academy of Science University of Chinese Academy of Sciences School of Computer Science and Engineering Southwest Minzu University

Due to the probability characteristics of quantum mechanism, the combination of quantum mechanism and intelligent algorithm has received wide attention. Quantum dynamics theory uses the Schr?dinger equation as a quantum dynamics equation. Through three approximation of the objective function, quantum dynamics framework(QDF) is obtained which describes basic iterative operations of optimization algorithms. Based on QDF, this paper proposes a potential barrier estimation(PBE) method which originates from quantum mechanism. With the proposed method, the particle can accept inferior solutions during the sampling process according to a probability which is subject to the quantum tunneling effect, to improve the global search capacity of optimization *** effectiveness of the proposed method in the ability of escaping local minima was thoroughly investigated through double well function(DWF), and experiments on two benchmark functions sets show that this method significantly improves the optimization performance of high dimensional complex functions. The PBE method is quantized and easily transplanted to other algorithms to achieve high performance in the future.

关键词： Potential energy Electric potential Shape Heuristic algorithms Estimation Tunneling Linear programming Approximation algorithms Iterative algorithms Optimization

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Adaptive coupled-sliding-variable-based finite-time control of composite formation for multi-robot systems

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science China(Information sciences) 2024年第10期67卷 244-260页

作者： Xinru MA Hengyu LI Jun LIU Yueying WANG Shaorong XIE Jun LUO School of Mechatronic Engineering and Automation Shanghai University School of Mathematics and Computer Application Technology Jining University School of Computer Science Shanghai University College of Mechanical and Vehicle Engineering Chongqing University

This paper focuses on the finite-time control(FTC) of the composite formation consensus(CFC)problems for multi-robot systems(MRSs). The CFC problems are firstly proposed for MRSs under the complex network topology of cooperative or cooperative-competitive networks. Regarding the problems of FTC and CFC on multiple Lagrange systems(MLSs), coupled sliding variables are introduced to deal with the robustness and consistent convergence. Then, the adaptive finite-time protocols are given based on the displacement approaches. With the premised FTC, tender-tracking methods are further developed for the problems of tracking information disparity. Stability analyses of those MLSs mentioned above are clarified with Lyapunov candidates considering the coupled sliding vectors, which provide new verification for tender-tracking systems. Under the given coupled-sliding-variable-based finite-time protocols, MLSs distributively adjust the local formation error to achieve global CFC and perform uniform convergence in time-varying tracking. Finally, simulation experiments are conducted while providing practical solutions for the theoretical results.

关键词： composite formation time-vary tracking information disparity tender-tracking finite-time control nonlinear robots complex networks symmetry consensus

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Machine Learning-Driven Economic Modeling for Enhanced Unemployment Rate Prediction and Analysis 16

Machine Learning-Driven Economic Modeling for Enhanced Unemp...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Arora, Rakhi Dixit, Nitin Sihare, Muskan Kinger, Shreshtha Shivhare, Sneha Kulshreshtha, Komal Institute of Technology & Management Computer Science and Engineering Gwalior India Masters of Computer Application Vikrant University Gwalior India

ISBN: (纸本)9798331505264

This study presents a comprehensive analysis of economic modeling using Python, specifically focusing on the prediction of unemployment rates. In response to the growing significance of data-driven decision-making in economics, our research explores the application of logistic regression and random forest classification models to comprehend and forecast unemployment status. The dataset utilized in this analysis encompasses historical unemployment rate data, serving as a valuable source for our modeling endeavors. Our methodology begins with logistic regression, establishing a baseline model to scrutinize the relationship between diverse economic indicators and the binary classification of unemployment status (above or below 10%). We evaluate the model's efficacy in capturing trends over time and identifying influential factors in determining unemployment status. Subsequently, we advance our approach by employing random forest classification, a sophisticated machine learning technique adept at capturing non-linear relationships and interactions within the data. Our investigation shows that both random forest classification and logistic regression are good at predicting jobless status. We conduct a comparative evaluation of these models, shedding light on their respective strengths and limitations. Additionally, we delve into the significance of feature selection, hyperparameter tuning, and model evaluation within the realm of economic modeling using Python. © 2024 IEEE.

关键词： Logistic regression

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A Collaborative Framework for Intrusion Detection in Cloud Computing Based on HLA-CNN-BiLSTM-SVM Model

A Collaborative Framework for Intrusion Detection in Cloud C...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. Department of Computer Science & Application School of Computer Science and Engineering Sandip University Nashik India

ISBN: (纸本)9798350374933

In addition to describing a way to make better use of preexisting computer infrastructures, the phrase "cloud computing" describes a technology that facilitates the sale of access to shared computer resources and services. The flip side is that these dispersed and convoluted infrastructures are attractive targets for attackers. Because cyberattacks can reduce the quality of service offered to clients, the system constitute a substantial risk. This proposed method consists of three steps like preprocessing, feature selection, and model training. Data preprocessing, which includes both feature discretization and feature normalization, is a critical step in any knowledge discovery process. In this proposed to evaluate the impact of feature selection on improving classifier algorithm accuracy by comparing two feature selection methods: one that takes linear correlation into account, and another that takes mutual information into account. When training the model, an HLA-CNN-BiLSTM-SVM was utilized. With a 92.35 percent average accuracy, the suggested method surpasses CNN-SVM and BiLSTM. © 2024 IEEE.

关键词： Network intrusion

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Deep Learning Techniques for Weather Prediction

Deep Learning Techniques for Weather Prediction

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhanalakshmi, R. Paul, Tushar School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India Masters of Computer Application Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331510022

Human activities are directly impacted by weather occurrences. Forest fires, high air temperatures that lead to drought, and extreme weather events caused by global warming all make life more difficult for humans. Precautions against such climatic calamities require weather forecast models that are both accurate and effective. As a result, creating models that accurately forecast the weather is crucial. The creation of effective models of deep learning-based weather forecasting was greatly aided by technological advancements. This study suggested a hybrid weather prediction model based on convolutional and recurrent neural networks, which had a decent success rate. The dataset named Jena, which comprises 14 parameters, extensive meteorological information that has not been used for predicting the weather, was subjected to the suggested hybrid model. The experimental findings were compared using well-known machine learning, deep learning, and statistical techniques such CNN, Integrated Moving Average with Auto-Regression, LSTM, RNN, Random Forest, Multilayer Perceptrons and SVM. These comparisons showed that the suggested hybrid model had the best prediction outcome across the board. For instance, the suggested hybrid model produced the following findings based on the weather forecast data for Jena. © 2024 IEEE.

关键词： Convolutional neural networks

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A Study Of Cyclone Tracking Mechanism Using Deep Learning Techniques

A Study Of Cyclone Tracking Mechanism Using Deep Learning Te...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Paul, Tushar Dhanalakshmi, R. Masters of Computer Application School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India

ISBN: (纸本)9798350315127

With over 90 storms emerging worldwide each year, tropical cyclones (TCs) are the most damaging weather systems to emerge over the tropical oceans. For the purpose of providing advanced warning to the impacted areas, fast TC detection and tracking is essential. Remote sensing is essential for the detection of these storms since they originate over open oceans that are far from the continents. Here, we describe an innovative deep learning approach-based automated technique for TC detection from satellite photos. Our research presents a three-phase deep learning architecture for TC detection that consists of three components: 1) a classifier - convolutional neural network (CNN);2) a wind speed filter;and 3) a detector - Mask convolutional neural network (CNN). Using Bayesian optimization, the hyperparameters of the pipeline as a whole are tuned to display optimal performance. Results show that the suggested method produces test images with specificity (97.59%), high precision (97.10%) and accuracy (86.55%). © 2023 IEEE.

关键词： Tropical cyclone

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Smart cities and the IoT: an in-depth analysis of global research trends and future directions

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Discover Internet of Things 2024年第1期4卷 19页

作者： Bhardwaj, Vivek Anooja, A. Vermani, Lovkesh Singh Sunita Dhaliwal, Balwinder Kaur School of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India School of Computer Application JECRC University Rajasthan Jaipur India Multani Mal Modi College Punjab Patiala India Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

Extensive scientific investigation is necessary because every government wants to construct smart cities. This is why examining how researchers approach this area of study is critical. This study investigates global research trends in smart cities and the Internet of Things (IoT) by analyzing 14,309 articles from the Scopus database (2010–2024). Using text mining and latent semantic analysis (LSA) via the KNIME tool, the research identifies key areas and trends in smart city development. The k-means clustering algorithm predicts future research directions, highlighting the most active countries, influential authors, and significant sources in the field. The results underscore the need for additional research in sectors where IoT applications for smart cities are still in their early stages. This analysis also offers insights into fostering international collaborations among institutions and researchers. The findings suggest that future research should focus on developing secure, scalable solutions to address challenges across various industries. Overall, this study provides a comprehensive overview of smart city research, offering valuable guidance for researchers and policymakers aiming to advance the integration of IoT technologies into urban environments. © The Author(s) 2024.

关键词： Latent semantic analysis

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Pruning Optimization of Deep Learning Model Based on Generalization Estimation

Pruning Optimization of Deep Learning Model Based on General...

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2024 International Conference on Artificial Intelligence, Deep Learning and Neural Networks, AIDLNN 2024

作者： Li, Xuanzhe Ke, Wenjun Wen, Wantao Liu, Shaotong Sheng, Xiaojuan School of Computer Science and Engineering Southeast University China Beijing Institute of Computer Technology and Application China

ISBN: (纸本)9798331520816

Existing research has shown that deep learning models contain considerable redundancy, requiring compression optimization to eliminate unnecessary parameters and improve computational efficiency. In model compression through pruning, many existing methods often use loss values to guide the identification of sub-network models. However, low training loss does not necessarily indicate good generalization ability. Consequently, reducing model parameters while preserving strong generalization performance has become a key area of research. Addressing the challenge of ensuring strong generalization performance of submodels after optimization through deep learning model pruning, we propose a channel-structured generalization estimation model. The model architecture incorporates Fully Connected Neural Networks (FCNN) and Gated Recurrent Units (GRU) as modules for channel feature extraction. Additionally, we introduce a differentiable pruning algorithm based on generalization estimation. Experimental results demonstrate the capability of the channel-structured generalization estimation model in predicting the generalization gap via regression. Furthermore, the proposed differentiable pruning algorithm effectively safeguards the generalization capability of sub-models. We conduct pruning experiments on NiN-structured convolutional neural network models on two datasets, CIFAR-10 and SVHN. Comparative experimental results demonstrate the effectiveness of this approach: achieving a 0.72% accuracy improvement over the original model on CIFAR-10;and a 0.12% accuracy improvement over the base model on SVHN while reducing FLOPs by 50.2%, validating the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Contrastive Learning

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Review on Predicting the Landslide Susceptibility using Machine Learning Techniques 2

Review on Predicting the Landslide Susceptibility using Mach...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sinha, Abhishek Giri, Shivam Kumar Yadav, Himanshu Prasad, Sanjeev Kumar Mishra, Priya School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Application ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

This Landslide susceptibility prediction is a critical task for Alleviating the risks associated with landslide hazards, which have significant socio-economic and environmental impacts. Existing techniques face challenges related to data quality, model coherence, and generalizability across diverse geographical and climatic regions. Additionally, while advanced machine learning (ML) and deep learning (DL) models offer high accuracy, they often require significant computational resources, which limits their practical application in real-time scenarios. In this review, we aim to highlight the strengths, limitations, and potential improvements of machine learning techniques in predicting landslide susceptibility, providing a comprehensive reference for researchers and practitioners in this domain. Furthermore, the role of Explainable AI is examined to address the critical need for interpretable and transparent models in real-world applications. Hybrid ML-DL models outperform traditional methods in handling complex spatial-temporal data, with dynamic factors improving accuracy and reliability. Explainable AI enhances interpretability, while advancements and collaborations address data and generaliz ability challenges. © 2025 IEEE.

关键词： Adversarial machine learning

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Real-time distance field acceleration based free-viewpoint video synthesis for large sports fields

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Computational Visual Media 2024年第2期10卷 331-353页

作者： Yanran Dai Jing Li Yuqi Jiang Haidong Qin Bang Liang Shikuan Hong Haozhe Pan Tao Yang School of Telecommunications Engineering Xidian UniversityXi’an 710071China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology SAIIPthe School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710129China

Free-viewpoint video allows the user to view objects from any virtual perspective,creating an immersive visual *** technology enhances the interactivity and freedom of multimedia ***,many free-viewpoint video synthesis methods hardly satisfy the requirement to work in real time with high precision,particularly for sports fields having large areas and numerous moving *** address these issues,we propose a freeviewpoint video synthesis method based on distance field *** central idea is to fuse multiview distance field information and use it to adjust the search step size *** step size search is used in two ways:for fast estimation of multiobject three-dimensional surfaces,and synthetic view rendering based on global occlusion *** have implemented our ideas using parallel computing for interactive display,using CUDA and OpenGL frameworks,and have used real-world and simulated experimental datasets for *** results show that the proposed method can render free-viewpoint videos with multiple objects on large sports fields at 25 ***,the visual quality of our synthetic novel viewpoint images exceeds that of state-of-the-art neural-rendering-based methods.

关键词： free-viewpoint video view synthesis camera array distance field sports video

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